Online or onsite, instructor-led live Reinforcement Learning training courses demonstrate through interactive hands-on practice how to create and deploy a Reinforcement Learning system.
Reinforcement Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Reinforcement Learning training can be carried out locally on customer premises in Porto or in NobleProg corporate training centers in Porto.
NobleProg -- Your Local Training Provider
Porto
NobleProg Porto, Av. da Boavista 1466, Porto, portugal, 4100-114
Our Venue is located on the city center of Porto, well connect by public transportation, with easy access by car with many op...
Our Venue is located on the city center of Porto, well connect by public transportation, with easy access by car with many options of parking places around the area. Surrounded by a large number of restaurants and commercial centres.
Holiday Inn Porto - Gaia
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Hotel Central Parque
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Hotel Central Parque **** is located in the center of the city of Maia, due to its proximity to Francisco Sá Carneiro Airpor...
Hotel Central Parque **** is located in the center of the city of Maia, due to its proximity to Francisco Sá Carneiro Airport, Zona Industrial da Maia, Exponor Matosinhos, the metro station and the main highways, it becomes the place ideal for your stay.
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Whether you travel for business, leisure or bleisure, Four Points by Sheraton Matosinhos welcomes you with four-star comfort, outstanding service and an ideal location in the city. The hotel offers a wide variety of facilities and services including a business center. Matosinhos beach is only 5 minutes walking distance from the hotel, perfect for surfing and other water sports. As a fishing area, local restaurants offer you the most wonderful fresh fishes and sea food for your delight.
This instructor-led, live training in Porto (online or onsite) is aimed at intermediate-level data scientists who wish to gain a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).By the end of this training, participants will be able to:
Understand the components and functionality of transformer models.
Optimize and fine-tune LLMs for specific tasks and applications.
Understand the core principles and methodologies of reinforcement learning.
Learn how reinforcement learning techniques can enhance the performance of LLMs.
This instructor-led, live training in Porto (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
Apply advanced Reinforcement Learning algorithms to solve real-world problems.
This instructor-led, live training in Porto (online or onsite) is aimed at data scientists who wish to go beyond traditional machine learning approaches to teach a computer program to figure out things (solve problems) without the use of labeled data and big data sets.
By the end of this training, participants will be able to:
Install and apply the libraries and programming language needed to implement Reinforcement Learning.
Create a software agent that is capable of learning through feedback instead of through supervised learning.
Program an agent to solve problems where decision making is sequential and finite.
Apply knowledge to design software that can learn in a way similar to how humans learn.
This instructor-led, live training in Porto (online or onsite) is aimed at data scientists who wish to create and deploy a Reinforcement Learning system, capable of making decisions and solving real-world problems within an organization.
By the end of this training, participants will be able to:
Understand the relationships and differences between Reinforcement Learning and machine learning, deep learning, supervised and unsupervised learning.
Analyze a real-world problem and redefine it as Reinforcement Learning problem.
Implementing a solution to a real-world problem using Reinforcement Learning.
Understand the different algorithms available in Reinforcement Learning and select the most suitable one for the problem at hand.
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